Are M&A Advisors Value Drivers in the European Construction Industry?

Table of Content

1. INTRODUCTION

2. THEORETICAL CONCEPTS AND LITERATURE REVIEW
2.1. THEORETICAL CONCEPTS
2.2. LITERATURE REVIEW
2.3. RESEARCH HYPOTHESES

3. M&A-ADVISERS IN THE EUROPEAN CONSTRUCTION INDUSTRY: EMPIRICAL EVIDENCE
3.1. DATA SAMPLE - SELECTION AND DESCRIPTION
3.2. EVENT STUDY METHODOLOGY
3.3. EMPIRICAL EVIDENCE FROM THE EUROPEAN CONSTRUCTION INDUSTRY
3.3.1. Hypothesis 1: Adviser Effect
3.3.2. Hypothesis 2: Reputation Effect
3.3.3. Hypothesis 3: Negotiation Effect
3.3.4. Hypothesis 4: Number of Adviser Involved
3.3.5. Multivariate Analysis
3.3.6. Combined Entity Analysis

4. SUMMARY AND CONCLUSION

1. Introduction

Over the last decade, there is an increasing involvement of financial advisers in European M&A-transactions. Since 1995 the involvement of M&A-advisers in transactions with European participation has more than doubled (16.4% in 1995; 34.7% in 2005). In approximately a quarter of all deals (mean percentage of deals from 1995- 2005), M&A-advisers were involved.¹ This raises the questions whether companies hiring M&A-advisers are better off and whether M&A-advisers can be seen as value drivers. The aforementioned questions have been rarely examined in empirical research. Furthermore, most of the existing studies focus on the US-American M&A-market. There has been little research on the European M&A-market and no research at all on the European construction industry in particular. Thus, the paper’s purpose is to examine the question whether M&A-advisers are value drivers in the European construction industry.

In the first part of this paper relevant theoretical concepts (2.1) are presented, existing literature is reviewed (2.2) and the research hypotheses are derived from theory (2.3). In the empirical part of the paper, the selection of the data sample (3.1) and the event study methodology (3.2) are first described. Then, the four hypotheses (adviser involvement, adviser reputation, negotiation effect and number of adviser involved) are tested followed by the multivariate analysis and the combined entity analysis (3.3). The paper is concluded by a summary of the findings including implications for further research (4).

2. Theoretical Concepts and Literature Review

2.1. Theoretical Concepts

The research question - are M&A-advisers value drivers in the European construction industry? - is a subcategory of the topic “the prominence of investment banks in M&Atransactions” (see Figure I).

Figure I: Structural Approach to Research Question and Research Design

illustration not visible in this excerpt

Source: Own depiction. Compare Beitel/Schiereck (2004), p. 436.

This topic is subdivided into two major subcategories: On the one hand, there is the key question how the existence of investment banks can be explained. Here, mainly transactional factors like the size of the transaction are examined (companies hire investment banks because the size of the transaction is too big to be handled on their own) or information asymmetries between management and owners are examined as a factor. In the other subcategory - the focus of this paper - investment banks are considered as potential value drivers because of their skills to identify potential bidders or targets, their negotiation skills, their advise on bidding strategy etc. These skills are viewed to be superior in comparison with those of potential clients because of the investment banks’ experience and their respective market knowledge. Regarding this value impact, studies have different focuses. Some studies focus on the payment of fees and premia.² As there is a lack of information on adviser fees and the premia paid, this paper focuses only on the cumulative abnormal return (CAR) of shareholders. Whereas the premium paid is also integrated in the study because the premium is indirectly measured by the CAR of the target. The CAR as indicator of wealth creation includes all means to increase the value of a company and is an objective measure assuming that the market is efficient. The application of CAR as measure for success of a transaction allows to examine the research question from three perspectives - from the bidder’s, target’s and combined entity’s view.

2.2. Literature Review

The role of M&A-advisers as value drivers has been rarely examined by empirical research. The majority of studies is focusing on the US-American M&A-market.³ There is no study that focuses on the European Construction Industry in particular. LOWINSKI, et al. (2004), BEITEL, et al. (2003) and BEITEL/SCHIERECK (2003) are some of the few European studies that have examined the value impact of investment banks on M&A- transactions.⁴ They all find no wealth effect of investment bank participation or rather very little impact. BEITEL/SCHIERECK/UNVERHAU (2003) have examined both bidder and target side. LOWINSKI/SCHIERECK/THOMAS (2004) and BEITEL/SCHIERECK (2003) have focused on the bidder side. KALE, et al. (2003) found in their study that there is a positive correlation of the total wealth created in the transaction and the reputation of the bidder’s and target’s advisers.⁵ BOWERS/MILLER (1990) support this finding for the combined view.⁶ In contrast, RAU (2000) shows that reputation of investment banks has no wealth effects.⁷ HUNTER/JAGTIANI (2003) even found in their study that the synergistic gains of the bidder declined when top-tier advisers were involved.⁸

In conclusion, there is mixed evidence on the role of financial advisers and their impact on value generation in M&A-transactions. The relevance of existing literature for the paper becomes more apparent in the following section in which the research hypotheses are presented.

2.3. Research Hypotheses

The research question (“Are M&A-advisers value drivers in the European construction industry?") is examined by the analysis of the following four hypotheses:

H1: Adviser involvement - The involvement of an adviser should yield higher returns for both bidder and target.

H2: Adviser reputation - The reputation of an investment bank will positively effect the value generation for bidder and target.

H3: Other’s party adviser - In case both sides employ advisers, the value generated for the own company is less than in case only the own party works with an adviser. H4: Number of Advisers - The more advisers are involved, the less value is generated for bidder and target.

H1 and H2 are derived from two theoretical concepts which are often found in literature: The superior deal hypothesis and the bargaining advantage hypothesis. The superior deal hypothesis implies that an investment bank has superior skills regarding the search for the most suitable bidder or target. Accordingly, the respective overall synergistic gains of the transaction increase. The search conducted by the investment bank is more efficient because of the superior market knowledge gained from experience. Furthermore, investment banks can exploit economies of scale by advising numerous clients, i.e. search costs decline. As a consequence of the competition in the investment banking industry, benefits of economies of scale are shared with clients. The bargaining advantage hypothesis stands for the superior negotiating skills of investment banks, i.e. the company who hired the M&A-adviser gets a larger share of the value generated in the transaction. However, the degree of competition in the market for acquisitions strongly influences the range of potential success in negotiations.⁹

In accordance with the aforementioned concepts, H1 states that the involvement of M&A-adviser has a positive impact on value generation. H2 implies that the better the reputation of an investment bank the better the skills of the adviser and as a consequence thereof the larger the wealth effect.

H3 is only derived from the bargaining advantage hypothesis. Unequally distributed negotiation skills may change the wealth distribution between bidder and target.

Additionally, information asymmetries about the real value of the company and better valuation skills might favour the party with adviser.

H4 is based on transaction cost theory. The more advisers and organisations are involved in the M&A-process, the higher the costs for coordination and communication; accordingly, marginal utility of advisers is lower than the costs incurred. In addition to the analysis of the four hypotheses, a multivariate analysis and an analysis of the combined entity are conducted.¹⁰

3. M&A-advisers in the European Construction Industry: Empirical Evidence

3.1. Data Sample - Selection and Description

The data sample comprises 171 transactions of the European construction industry; the deal data comes from Thomson Financial Securities Data (SDC). All considered deals are chosen by the following criteria:

ƒcontrol transaction (i.e. before the transaction the bidder owns less than 50% of the shares/assets of the target and after completion of the transaction more than 50%), ƒthe announcement day of the transaction is between 1/1/1995 and 31/12/2005, ƒ the involved companies are publicly traded, ƒthe deal value is at least $50 Mil., ƒ the deal status is completed, ƒthe business focus of target or bidder is mainly in the construction industry, ƒ the home region of at least one party is European.

The data sample does not show any surprises. The time horizon is restricted to the last ten years as before data on M&A-adviser is scarce and generally adviser activity was low. In only eleven out of 171 transactions an auditing company is hired as M&A- adviser. In all the other deals with involvement of M&A-advisers (135 transactions), investment banks are hired. As a consequence thereof, the terms M&A-adviser and investment bank are seen as synonyms within this paper. In half of all transactions, both bidder and target are advised by an investment bank. By comparing the deals in which only one side has hired an investment bank (34% of all deals), M&A-advisers are more often involved on the bidder side (55%). Regarding the bidder and target region, Europe is by far the leading region for both bidder and target side in terms of transaction volume and number of transactions - followed by America. The most represented industries in the sample are “building/construction and engineering” and “transportation and infrastructure”.¹¹

3.2. Event Study Methodology

In order to address the research questions propose above, short-term cumulative abnormal returns (CARs) of bidder, target and the combined entity will be analyzed. Given the rationality and information efficiency of the marketplace, the effects of an event will be reflected immediately in security prices shortly after new information is announced.¹² Thus, the difference between the expected return and the actual return, the so-called abnormal return, can be associated with the new information which was disclosed to the marketplace.¹³ The event date is defined as the announcement date of the takeover as stated in SDC database. In case this is not a trading day the first trading day after the announcement day is considered the event day.

As the event occurrence cannot be determined with complete certainty, the study therefore examines different event windows before and after the event date assuming that stock price reactions will spread over several days.¹⁴ In fact, eight event windows ([-20;20], [-10;10], [-5;5], [-1;1],0, [-20;-1], [1;20] and [-4;2]) will be calculated to cover different time horizons.

To calculate the expected return a market model approach is applied. The parameters of the market model are calculated by an ordinary least squares regression (OLS) with daily returns as suggested by BROWN/WARNER (1985):¹⁵

Equation I: Market Model

illustration not visible in this excerpt

where Rit is the return on stock i for day t, Įi and ȕi are the intercept and slope coefficient of stock i from the regression, Rmt is the market index return for period t, İit is the disturbance term and ı²İ is the variance of the disturbance terms. The parameters are estimated during a period of 200 trading days prior to the event window ranging from -220 to -21 days relative to the event day.¹⁶ Potential biases in the parameters arising from infrequent trading are not applied as stocks with infrequent trading are excluded from the study.¹⁷ For European companies the relevant market index is defined as the DJ EURO STOXX Construction & Materials assuming that a broad sector index best describes the general sector development. Accordingly, non- European companies are measured against the MSCI World Construction & Engineering index.¹⁸ Having calculated the market model and the expected return in Equation I, the abnormal return can be defined as:

Equation II: Abnormal Return Calculation

illustration not visible in this excerpt

where ARit is the abnormal return of stock i for period t and the term in the brackets is the expected return of the market model. The aggregation of the abnormal returns can be calculated along two dimensions - across securities and through time.¹⁹ The average abnormal return on a given day t (AARt) for a portfolio of n securities is computed by:

Equation III: Average Abnormal Return (AAR)

illustration not visible in this excerpt

The cumulative abnormal return (CAR) for different event windows [T1;T2] is

calculated by:

Equation IV: Cumulative Abnormal Return (CAR)

illustration not visible in this excerpt

The calculation of the CARs is followed by the hypotheses testing according to the research hypotheses. This will be conducted by a sub-sample analysis grouping stocks into two sub samples for each hypothesis. Additionally, a multiple cross-sectional regression analysis for the time window [-20;20] will test whether the results from the hypotheses still hold true in a joined model. Furthermore the analysis will also cover the combined wealth gain from the transaction, which is the combination of bidder and target wealth gains using the respective market capitalization 20 days prior to the announcement (MVi):²⁰

Equation V: Combined Entity Abnormal Return

illustration not visible in this excerpt

In order to verify the statistical significance of the obtained CARs, it is necessary to apply test statistics, which indicate whether the results observed in the sample are also valid for the whole population of acquisitions. To overcome statistical misspecifications due to variance shifts between estimation period and event period, standardized abnormal returns are calculated using the approach developed by Dodd/Warner.²¹ The daily individual abnormal returns (ARit) are standardized through dividing it by the estimated standard error of the forecast (sift):

Equation VI: Standard Error of the Forcast

illustration not visible in this excerpt

with sie = the standard error of the estimation period; Ti = the number of days employed in the estimation period; RmE = market return for day E in the event period; Rm = average market return for the estimation period; Rmt = market return for day t in the estimation period.

Accounting for event-induced variance, the study calculates a test statistic which was developed by Boehmer.²² The test statistic further adjusts the standardized abnormal returns in a standardized cross-sectional approach.

[...]

¹ This analysis is based on financial data from Thomson Financial SDC. Cf. also Appendix I.

² Cf. BEITEL/SCHIERECK (2004), pp. 436-450.

³ Cf. Appendix II.

⁴ Cf. LOWINSKI/SCHIERECK/THOMAS (2004), p. 315; BEITEL/SCHIERECK/UNVERHAU (2003), pp. 17-19; BEITEL/SCHIERECK (2003), pp. 20f.

⁵ Cf. KALE/KINI/RYAN (2003), p. 475.

⁶ Cf. BOWERS/MILLER (1990), p. 43.

⁷ Cf. RAU (2000), p. 322.

⁸ Cf. HUNTER/JAGTIANI (2003), p. 65.

⁹ Cf. BEITEL/SCHIERECK/UNVERHAU (2003), pp. 4-7; BOWERS/MILLER (1990), p. 36.

¹⁰ The multivariate analysis examines whether the findings regarding the hypotheses are influenced by factors such as type of payment (in cash or shares) and type of transaction (cross-border or domestic). The analysis of the combined entity comprises H1 to H3.

¹¹ Cf. Appendix III-VI.

¹² Cf. MACKINLAY (1997), p. 13.

¹³ Cf. PETERSON (1989), p. 36.

¹⁴ Cf. BROWN/WARNER (1980), pp. 224-227.

¹⁵ Cf. BROWN/WARNER (1985), p. 25, based on FAMA/FISHER/JENSEN/ROLL (1969), pp. 3f.

¹⁶ Cf. PETERSON (1989), p. 38.

¹⁷ Moreover, BROWN/WARNER (1985), p. 26 find that procedures to reduce biases in OLS estimates do not improve the power of the actual tests for abnormal performance.

¹⁸ Doing so, international valuation differences are reflected in the benchmark.

¹⁹ For an overview of the aggregation of abnormal returns, cf. PETERSON (1989), pp. 45-48.

²⁰ Cf. BRADLEY/DESAI/KIM (1988), pp. 9f.

²¹ Cf. DODD/WARNER (1983), pp. 412-414.

²² Cf. BOEHMER/MASCUMECI/POULSEN (1991), p. 270.